In this paper, we propose a new intelligent image processing technique, denoted as Wavelet-Cellular Neural Network (Wave-CNN) which can be applied to estimate the borders of buried objects. In Wave-CNN, we benefit from Cellular Neural Network (CNN) and wavelet techniques. Thus, Wave-CNN uses advantages of both CNN and wavelet approaches and offers the ability of supervised geophysical image processing such as residual-regional separation, de-noising, border detection and image enhancement. We evaluate border detection performance of Wave-CNN technique in synthetic examples. Upon the satisfactory results in the synthetic models, the proposed technique is applied to real archaeological data belongs to Hittite Empire Sites to detect borders of historical walls. The results are also compared with Boundary analyze method and classical filters.